Overview

Brought to you by YData

Dataset statistics

Number of variables35
Number of observations1852394
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory482.3 MiB
Average record size in memory273.0 B

Variable types

Numeric21
Text8
Categorical4
DateTime1
Boolean1

Alerts

amt_month is highly overall correlated with amt_month_shopping_net_spend and 1 other fieldsHigh correlation
amt_month_shopping_net_spend is highly overall correlated with amt_month and 1 other fieldsHigh correlation
amt_year is highly overall correlated with trans_monthHigh correlation
count_month_shopping_net is highly overall correlated with amt_month and 1 other fieldsHigh correlation
first_time_at_merchant is highly overall correlated with unix_timeHigh correlation
lat is highly overall correlated with merch_latHigh correlation
long is highly overall correlated with merch_long and 1 other fieldsHigh correlation
merch_lat is highly overall correlated with latHigh correlation
merch_long is highly overall correlated with long and 1 other fieldsHigh correlation
times_shopped_at_merchant is highly overall correlated with times_shopped_at_merchant_yearHigh correlation
times_shopped_at_merchant_year is highly overall correlated with times_shopped_at_merchantHigh correlation
trans_month is highly overall correlated with amt_yearHigh correlation
unix_time is highly overall correlated with first_time_at_merchant and 1 other fieldsHigh correlation
year is highly overall correlated with unix_timeHigh correlation
zip is highly overall correlated with long and 1 other fieldsHigh correlation
is_fraud is highly imbalanced (95.3%) Imbalance
amt is highly skewed (γ1 = 40.81280918) Skewed
trans_num has unique values Unique
dist_between_client_and_merch has unique values Unique
amt_month_shopping_net_spend has 276206 (14.9%) zeros Zeros
count_month_shopping_net has 276206 (14.9%) zeros Zeros
trans_day has 369418 (19.9%) zeros Zeros
hour has 60655 (3.3%) zeros Zeros

Reproduction

Analysis started2025-04-21 06:48:03.206505
Analysis finished2025-04-21 06:56:19.751789
Duration8 minutes and 16.55 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

cc_num
Real number (ℝ)

Distinct999
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1738604 × 1017
Minimum6.0416207 × 1010
Maximum4.9923464 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:20.291423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.0416207 × 1010
5-th percentile6.3048488 × 1011
Q11.8004295 × 1014
median3.5214173 × 1015
Q34.6422555 × 1015
95-th percentile4.497914 × 1018
Maximum4.9923464 × 1018
Range4.9923463 × 1018
Interquartile range (IQR)4.4622125 × 1015

Descriptive statistics

Standard deviation1.3091153 × 1018
Coefficient of variation (CV)3.1364616
Kurtosis6.1753558
Mean4.1738604 × 1017
Median Absolute Deviation (MAD)3.0764709 × 1015
Skewness2.8510736
Sum5.0088429 × 1018
Variance1.7137828 × 1036
MonotonicityNot monotonic
2025-04-21T00:56:20.529260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.02704321 × 10134392
 
0.2%
6.538441737 × 10154392
 
0.2%
4.642255475 × 10154386
 
0.2%
6.538891243 × 10154386
 
0.2%
4.364010865 × 10154386
 
0.2%
6.011438889 × 10154385
 
0.2%
3.447098678 × 10144385
 
0.2%
4.512828415 × 10184384
 
0.2%
4.586810169 × 10154384
 
0.2%
4.745996322 × 10124384
 
0.2%
Other values (989) 1808530
97.6%
ValueCountFrequency (%)
6.041620718 × 10102196
0.1%
6.042292873 × 10102200
0.1%
6.042309813 × 1010738
 
< 0.1%
6.042785159 × 1010743
 
< 0.1%
6.048700208 × 1010735
 
< 0.1%
6.04905963 × 10101465
0.1%
6.049559311 × 1010742
 
< 0.1%
5.018029536 × 10112194
0.1%
5.018181333 × 10118
 
< 0.1%
5.018282048 × 1011733
 
< 0.1%
ValueCountFrequency (%)
4.992346398 × 10182922
0.2%
4.989847571 × 10181471
0.1%
4.980323468 × 1018736
 
< 0.1%
4.973530368 × 10181467
0.1%
4.958589672 × 10182191
0.1%
4.95682899 × 10183657
0.2%
4.911818931 × 10189
 
< 0.1%
4.906628656 × 10183655
0.2%
4.897067971 × 10181471
0.1%
4.890424427 × 10182189
0.1%
Distinct693
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:21.008600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length43
Median length36
Mean length23.130553
Min length13

Characters and Unicode

Total characters42846898
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfraud_Rippin, Kub and Mann
2nd rowfraud_Heller, Gutmann and Zieme
3rd rowfraud_Lind-Buckridge
4th rowfraud_Kutch, Hermiston and Farrell
5th rowfraud_Keeling-Crist
ValueCountFrequency (%)
and 677362
 
15.7%
llc 139662
 
3.2%
inc 131148
 
3.0%
sons 104651
 
2.4%
ltd 100896
 
2.3%
plc 94799
 
2.2%
group 72089
 
1.7%
fraud_kutch 15028
 
0.3%
fraud_schaefer 13367
 
0.3%
fraud_streich 13235
 
0.3%
Other values (804) 2956186
68.5%
2025-04-21T00:56:21.582984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4158232
 
9.7%
r 3851348
 
9.0%
d 3055994
 
7.1%
e 2665745
 
6.2%
u 2654462
 
6.2%
n 2526397
 
5.9%
2466029
 
5.8%
f 1996096
 
4.7%
_ 1852394
 
4.3%
o 1614017
 
3.8%
Other values (45) 16006184
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42846898
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4158232
 
9.7%
r 3851348
 
9.0%
d 3055994
 
7.1%
e 2665745
 
6.2%
u 2654462
 
6.2%
n 2526397
 
5.9%
2466029
 
5.8%
f 1996096
 
4.7%
_ 1852394
 
4.3%
o 1614017
 
3.8%
Other values (45) 16006184
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42846898
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4158232
 
9.7%
r 3851348
 
9.0%
d 3055994
 
7.1%
e 2665745
 
6.2%
u 2654462
 
6.2%
n 2526397
 
5.9%
2466029
 
5.8%
f 1996096
 
4.7%
_ 1852394
 
4.3%
o 1614017
 
3.8%
Other values (45) 16006184
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42846898
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4158232
 
9.7%
r 3851348
 
9.0%
d 3055994
 
7.1%
e 2665745
 
6.2%
u 2654462
 
6.2%
n 2526397
 
5.9%
2466029
 
5.8%
f 1996096
 
4.7%
_ 1852394
 
4.3%
o 1614017
 
3.8%
Other values (45) 16006184
37.4%

category
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.1 MiB
gas_transport
188029 
grocery_pos
176191 
home
175460 
shopping_pos
166463 
kids_pets
161727 
Other values (9)
984524 

Length

Max length14
Median length12
Mean length10.525913
Min length4

Characters and Unicode

Total characters19498139
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmisc_net
2nd rowgrocery_pos
3rd rowentertainment
4th rowgas_transport
5th rowmisc_pos

Common Values

ValueCountFrequency (%)
gas_transport 188029
10.2%
grocery_pos 176191
9.5%
home 175460
9.5%
shopping_pos 166463
9.0%
kids_pets 161727
8.7%
shopping_net 139322
7.5%
entertainment 134118
7.2%
food_dining 130729
 
7.1%
personal_care 130085
 
7.0%
health_fitness 122553
 
6.6%
Other values (4) 327717
17.7%

Length

2025-04-21T00:56:21.758878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gas_transport 188029
10.2%
grocery_pos 176191
9.5%
home 175460
9.5%
shopping_pos 166463
9.0%
kids_pets 161727
8.7%
shopping_net 139322
7.5%
entertainment 134118
7.2%
food_dining 130729
 
7.1%
personal_care 130085
 
7.0%
health_fitness 122553
 
6.6%
Other values (4) 327717
17.7%

Most occurring characters

ValueCountFrequency (%)
s 2042254
10.5%
e 1838696
9.4%
o 1758769
9.0%
n 1705118
8.7%
p 1548294
 
7.9%
t 1538055
 
7.9%
_ 1484860
 
7.6%
r 1310440
 
6.7%
i 1190524
 
6.1%
a 950855
 
4.9%
Other values (10) 4130274
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19498139
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 2042254
10.5%
e 1838696
9.4%
o 1758769
9.0%
n 1705118
8.7%
p 1548294
 
7.9%
t 1538055
 
7.9%
_ 1484860
 
7.6%
r 1310440
 
6.7%
i 1190524
 
6.1%
a 950855
 
4.9%
Other values (10) 4130274
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19498139
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 2042254
10.5%
e 1838696
9.4%
o 1758769
9.0%
n 1705118
8.7%
p 1548294
 
7.9%
t 1538055
 
7.9%
_ 1484860
 
7.6%
r 1310440
 
6.7%
i 1190524
 
6.1%
a 950855
 
4.9%
Other values (10) 4130274
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19498139
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 2042254
10.5%
e 1838696
9.4%
o 1758769
9.0%
n 1705118
8.7%
p 1548294
 
7.9%
t 1538055
 
7.9%
_ 1484860
 
7.6%
r 1310440
 
6.7%
i 1190524
 
6.1%
a 950855
 
4.9%
Other values (10) 4130274
21.2%

amt
Real number (ℝ)

Skewed 

Distinct60616
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.063567
Minimum1
Maximum28948.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:21.950801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.44
Q19.64
median47.45
Q383.1
95-th percentile195.34
Maximum28948.9
Range28947.9
Interquartile range (IQR)73.46

Descriptive statistics

Standard deviation159.25397
Coefficient of variation (CV)2.2729927
Kurtosis4181.9073
Mean70.063567
Median Absolute Deviation (MAD)37.46
Skewness40.812809
Sum1.2978533 × 108
Variance25361.828
MonotonicityNot monotonic
2025-04-21T00:56:22.173709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.14 779
 
< 0.1%
1.1 745
 
< 0.1%
1.04 744
 
< 0.1%
1.08 741
 
< 0.1%
1.25 737
 
< 0.1%
1.2 737
 
< 0.1%
1.02 736
 
< 0.1%
1.01 735
 
< 0.1%
1.22 727
 
< 0.1%
1.03 726
 
< 0.1%
Other values (60606) 1844987
99.6%
ValueCountFrequency (%)
1 332
< 0.1%
1.01 735
< 0.1%
1.02 736
< 0.1%
1.03 726
< 0.1%
1.04 744
< 0.1%
1.05 721
< 0.1%
1.06 671
< 0.1%
1.07 723
< 0.1%
1.08 741
< 0.1%
1.09 720
< 0.1%
ValueCountFrequency (%)
28948.9 1
< 0.1%
27390.12 1
< 0.1%
27119.77 1
< 0.1%
26544.12 1
< 0.1%
25086.94 1
< 0.1%
22768.11 1
< 0.1%
21437.71 1
< 0.1%
19364.91 1
< 0.1%
17897.24 1
< 0.1%
16837.08 1
< 0.1%

first
Text

Distinct355
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:22.684088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length9
Mean length6.0802977
Min length3

Characters and Unicode

Total characters11263107
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJennifer
2nd rowStephanie
3rd rowEdward
4th rowJeremy
5th rowTyler
ValueCountFrequency (%)
christopher 38112
 
2.1%
robert 30743
 
1.7%
jessica 29236
 
1.6%
david 28564
 
1.5%
michael 28539
 
1.5%
james 28496
 
1.5%
jennifer 24181
 
1.3%
john 23445
 
1.3%
mary 23424
 
1.3%
william 23396
 
1.3%
Other values (345) 1574258
85.0%
2025-04-21T00:56:23.354354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1438618
 
12.8%
e 1230164
 
10.9%
i 883628
 
7.8%
n 877668
 
7.8%
r 867952
 
7.7%
l 554750
 
4.9%
h 493347
 
4.4%
s 463151
 
4.1%
t 444904
 
4.0%
o 384330
 
3.4%
Other values (39) 3624595
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11263107
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1438618
 
12.8%
e 1230164
 
10.9%
i 883628
 
7.8%
n 877668
 
7.8%
r 867952
 
7.7%
l 554750
 
4.9%
h 493347
 
4.4%
s 463151
 
4.1%
t 444904
 
4.0%
o 384330
 
3.4%
Other values (39) 3624595
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11263107
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1438618
 
12.8%
e 1230164
 
10.9%
i 883628
 
7.8%
n 877668
 
7.8%
r 867952
 
7.7%
l 554750
 
4.9%
h 493347
 
4.4%
s 463151
 
4.1%
t 444904
 
4.0%
o 384330
 
3.4%
Other values (39) 3624595
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11263107
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1438618
 
12.8%
e 1230164
 
10.9%
i 883628
 
7.8%
n 877668
 
7.8%
r 867952
 
7.7%
l 554750
 
4.9%
h 493347
 
4.4%
s 463151
 
4.1%
t 444904
 
4.0%
o 384330
 
3.4%
Other values (39) 3624595
32.2%

last
Text

Distinct486
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:23.837442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length10
Mean length6.1123751
Min length2

Characters and Unicode

Total characters11322527
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBanks
2nd rowGill
3rd rowSanchez
4th rowWhite
5th rowGarcia
ValueCountFrequency (%)
smith 40940
 
2.2%
williams 33661
 
1.8%
davis 31434
 
1.7%
johnson 28590
 
1.5%
rodriguez 24879
 
1.3%
martinez 21246
 
1.1%
jones 19825
 
1.1%
lewis 18293
 
1.0%
miller 16821
 
0.9%
gonzalez 16809
 
0.9%
Other values (476) 1599896
86.4%
2025-04-21T00:56:24.500639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1122673
 
9.9%
r 941641
 
8.3%
a 926704
 
8.2%
n 869662
 
7.7%
o 832319
 
7.4%
l 698286
 
6.2%
s 696904
 
6.2%
i 622878
 
5.5%
t 412730
 
3.6%
h 327959
 
2.9%
Other values (38) 3870771
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11322527
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1122673
 
9.9%
r 941641
 
8.3%
a 926704
 
8.2%
n 869662
 
7.7%
o 832319
 
7.4%
l 698286
 
6.2%
s 696904
 
6.2%
i 622878
 
5.5%
t 412730
 
3.6%
h 327959
 
2.9%
Other values (38) 3870771
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11322527
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1122673
 
9.9%
r 941641
 
8.3%
a 926704
 
8.2%
n 869662
 
7.7%
o 832319
 
7.4%
l 698286
 
6.2%
s 696904
 
6.2%
i 622878
 
5.5%
t 412730
 
3.6%
h 327959
 
2.9%
Other values (38) 3870771
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11322527
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1122673
 
9.9%
r 941641
 
8.3%
a 926704
 
8.2%
n 869662
 
7.7%
o 832319
 
7.4%
l 698286
 
6.2%
s 696904
 
6.2%
i 622878
 
5.5%
t 412730
 
3.6%
h 327959
 
2.9%
Other values (38) 3870771
34.2%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.1 MiB
F
1014749 
M
837645 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1852394
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
F 1014749
54.8%
M 837645
45.2%

Length

2025-04-21T00:56:24.659312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-21T00:56:24.786149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
f 1014749
54.8%
m 837645
45.2%

Most occurring characters

ValueCountFrequency (%)
F 1014749
54.8%
M 837645
45.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1852394
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 1014749
54.8%
M 837645
45.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1852394
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 1014749
54.8%
M 837645
45.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1852394
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 1014749
54.8%
M 837645
45.2%

street
Text

Distinct999
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:25.167226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length29
Mean length22.231289
Min length12

Characters and Unicode

Total characters41181107
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row561 Perry Cove
2nd row43039 Riley Greens Suite 393
3rd row594 White Dale Suite 530
4th row9443 Cynthia Court Apt. 038
5th row408 Bradley Rest
ValueCountFrequency (%)
apt 468297
 
6.4%
suite 437016
 
5.9%
island 32903
 
0.4%
michael 27058
 
0.4%
islands 25611
 
0.3%
station 25602
 
0.3%
common 25585
 
0.3%
david 24853
 
0.3%
brooks 24143
 
0.3%
fields 23400
 
0.3%
Other values (1959) 6253340
84.9%
2025-04-21T00:56:25.759438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5515414
 
13.4%
e 2561201
 
6.2%
a 2077034
 
5.0%
i 1851621
 
4.5%
t 1782137
 
4.3%
r 1576757
 
3.8%
n 1523518
 
3.7%
s 1476954
 
3.6%
l 1270600
 
3.1%
o 1251043
 
3.0%
Other values (52) 20294828
49.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41181107
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5515414
 
13.4%
e 2561201
 
6.2%
a 2077034
 
5.0%
i 1851621
 
4.5%
t 1782137
 
4.3%
r 1576757
 
3.8%
n 1523518
 
3.7%
s 1476954
 
3.6%
l 1270600
 
3.1%
o 1251043
 
3.0%
Other values (52) 20294828
49.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41181107
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5515414
 
13.4%
e 2561201
 
6.2%
a 2077034
 
5.0%
i 1851621
 
4.5%
t 1782137
 
4.3%
r 1576757
 
3.8%
n 1523518
 
3.7%
s 1476954
 
3.6%
l 1270600
 
3.1%
o 1251043
 
3.0%
Other values (52) 20294828
49.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41181107
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5515414
 
13.4%
e 2561201
 
6.2%
a 2077034
 
5.0%
i 1851621
 
4.5%
t 1782137
 
4.3%
r 1576757
 
3.8%
n 1523518
 
3.7%
s 1476954
 
3.6%
l 1270600
 
3.1%
o 1251043
 
3.0%
Other values (52) 20294828
49.3%

city
Text

Distinct906
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:26.188239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length21
Mean length8.6526209
Min length3

Characters and Unicode

Total characters16028063
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMoravian Falls
2nd rowOrient
3rd rowMalad City
4th rowBoulder
5th rowDoe Hill
ValueCountFrequency (%)
city 30780
 
1.3%
west 27847
 
1.2%
saint 20483
 
0.9%
north 20472
 
0.9%
falls 18286
 
0.8%
new 16857
 
0.7%
mount 16098
 
0.7%
lake 16089
 
0.7%
san 14638
 
0.6%
springs 12414
 
0.5%
Other values (929) 2118136
91.6%
2025-04-21T00:56:26.828160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1555978
 
9.7%
a 1334959
 
8.3%
n 1173952
 
7.3%
o 1168590
 
7.3%
l 1115539
 
7.0%
r 1070587
 
6.7%
i 1007053
 
6.3%
t 855511
 
5.3%
s 637587
 
4.0%
459706
 
2.9%
Other values (42) 5648601
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16028063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1555978
 
9.7%
a 1334959
 
8.3%
n 1173952
 
7.3%
o 1168590
 
7.3%
l 1115539
 
7.0%
r 1070587
 
6.7%
i 1007053
 
6.3%
t 855511
 
5.3%
s 637587
 
4.0%
459706
 
2.9%
Other values (42) 5648601
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16028063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1555978
 
9.7%
a 1334959
 
8.3%
n 1173952
 
7.3%
o 1168590
 
7.3%
l 1115539
 
7.0%
r 1070587
 
6.7%
i 1007053
 
6.3%
t 855511
 
5.3%
s 637587
 
4.0%
459706
 
2.9%
Other values (42) 5648601
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16028063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1555978
 
9.7%
a 1334959
 
8.3%
n 1173952
 
7.3%
o 1168590
 
7.3%
l 1115539
 
7.0%
r 1070587
 
6.7%
i 1007053
 
6.3%
t 855511
 
5.3%
s 637587
 
4.0%
459706
 
2.9%
Other values (42) 5648601
35.2%

state
Text

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:27.099589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3704788
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNC
2nd rowWA
3rd rowID
4th rowMT
5th rowVA
ValueCountFrequency (%)
tx 135269
 
7.3%
ny 119419
 
6.4%
pa 114173
 
6.2%
ca 80495
 
4.3%
oh 66627
 
3.6%
mi 65825
 
3.6%
il 62212
 
3.4%
fl 60775
 
3.3%
al 58521
 
3.2%
mo 54904
 
3.0%
Other values (41) 1034174
55.8%
2025-04-21T00:56:27.515558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 508580
13.7%
N 406389
 
11.0%
M 314756
 
8.5%
I 260547
 
7.0%
T 220136
 
5.9%
L 211461
 
5.7%
O 205755
 
5.6%
C 201235
 
5.4%
Y 188176
 
5.1%
X 135269
 
3.7%
Other values (14) 1052484
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3704788
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 508580
13.7%
N 406389
 
11.0%
M 314756
 
8.5%
I 260547
 
7.0%
T 220136
 
5.9%
L 211461
 
5.7%
O 205755
 
5.6%
C 201235
 
5.4%
Y 188176
 
5.1%
X 135269
 
3.7%
Other values (14) 1052484
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3704788
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 508580
13.7%
N 406389
 
11.0%
M 314756
 
8.5%
I 260547
 
7.0%
T 220136
 
5.9%
L 211461
 
5.7%
O 205755
 
5.6%
C 201235
 
5.4%
Y 188176
 
5.1%
X 135269
 
3.7%
Other values (14) 1052484
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3704788
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 508580
13.7%
N 406389
 
11.0%
M 314756
 
8.5%
I 260547
 
7.0%
T 220136
 
5.9%
L 211461
 
5.7%
O 205755
 
5.6%
C 201235
 
5.4%
Y 188176
 
5.1%
X 135269
 
3.7%
Other values (14) 1052484
28.4%

zip
Real number (ℝ)

High correlation 

Distinct985
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48813.258
Minimum1257
Maximum99921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:27.707206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1257
5-th percentile7208
Q126237
median48174
Q372042
95-th percentile94569
Maximum99921
Range98664
Interquartile range (IQR)45805

Descriptive statistics

Standard deviation26881.846
Coefficient of variation (CV)0.55070788
Kurtosis-1.0960542
Mean48813.258
Median Absolute Deviation (MAD)23068
Skewness0.078949647
Sum9.0421387 × 1010
Variance7.2263364 × 108
MonotonicityNot monotonic
2025-04-21T00:56:27.945564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82514 5116
 
0.3%
73754 5116
 
0.3%
48088 5115
 
0.3%
34112 5108
 
0.3%
61454 4392
 
0.2%
16114 4392
 
0.2%
84540 4386
 
0.2%
89512 4386
 
0.2%
72476 4386
 
0.2%
33872 4385
 
0.2%
Other values (975) 1805612
97.5%
ValueCountFrequency (%)
1257 2923
0.2%
1330 1466
0.1%
1535 734
 
< 0.1%
1545 1468
0.1%
1612 738
 
< 0.1%
1843 3652
0.2%
1844 2919
0.2%
2180 738
 
< 0.1%
2630 2924
0.2%
2908 745
 
< 0.1%
ValueCountFrequency (%)
99921 14
 
< 0.1%
99783 2203
0.1%
99747 12
 
< 0.1%
99746 734
 
< 0.1%
99323 3651
0.2%
99160 4362
0.2%
99116 15
 
< 0.1%
99113 1463
 
0.1%
99033 3646
0.2%
98836 740
 
< 0.1%

lat
Real number (ℝ)

High correlation 

Distinct983
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.539311
Minimum20.0271
Maximum66.6933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:28.153069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20.0271
5-th percentile29.8826
Q134.6689
median39.3543
Q341.9404
95-th percentile45.8433
Maximum66.6933
Range46.6662
Interquartile range (IQR)7.2715

Descriptive statistics

Standard deviation5.0714704
Coefficient of variation (CV)0.13159214
Kurtosis0.79107707
Mean38.539311
Median Absolute Deviation (MAD)3.3597
Skewness-0.19199899
Sum71389988
Variance25.719812
MonotonicityNot monotonic
2025-04-21T00:56:28.384430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.0048 5116
 
0.3%
36.385 5116
 
0.3%
42.5164 5115
 
0.3%
26.1184 5108
 
0.3%
41.3851 4392
 
0.2%
40.6761 4392
 
0.2%
36.0244 4386
 
0.2%
38.9999 4386
 
0.2%
39.5483 4386
 
0.2%
34.2853 4385
 
0.2%
Other values (973) 1805612
97.5%
ValueCountFrequency (%)
20.0271 2186
0.1%
20.0827 1463
 
0.1%
24.6557 3655
0.2%
26.1184 5108
0.3%
26.3304 741
 
< 0.1%
26.3771 732
 
< 0.1%
26.4215 4362
0.2%
26.4722 3650
0.2%
26.529 2202
0.1%
26.6939 1467
 
0.1%
ValueCountFrequency (%)
66.6933 12
 
< 0.1%
65.6899 734
 
< 0.1%
64.7556 2203
0.1%
55.4732 14
 
< 0.1%
48.8878 4362
0.2%
48.8856 2909
0.2%
48.8328 2200
0.1%
48.6669 1469
 
0.1%
48.6031 4376
0.2%
48.4786 2916
0.2%

long
Real number (ℝ)

High correlation 

Distinct983
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.227832
Minimum-165.6723
Maximum-67.9503
Zeros0
Zeros (%)0.0%
Negative1852394
Negative (%)100.0%
Memory size14.1 MiB
2025-04-21T00:56:28.599814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-165.6723
5-th percentile-119.0825
Q1-96.798
median-87.4769
Q3-80.158
95-th percentile-73.5365
Maximum-67.9503
Range97.722
Interquartile range (IQR)16.64

Descriptive statistics

Standard deviation13.747895
Coefficient of variation (CV)-0.15236867
Kurtosis1.8375586
Mean-90.227832
Median Absolute Deviation (MAD)8.1527
Skewness-1.1469188
Sum-1.671375 × 108
Variance189.00461
MonotonicityNot monotonic
2025-04-21T00:56:28.839192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-108.8964 5116
 
0.3%
-98.0727 5116
 
0.3%
-82.9832 5115
 
0.3%
-81.7361 5108
 
0.3%
-91.0391 4392
 
0.2%
-80.1752 4392
 
0.2%
-82.7243 4391
 
0.2%
-119.7957 4386
 
0.2%
-109.615 4386
 
0.2%
-90.9288 4386
 
0.2%
Other values (973) 1805606
97.5%
ValueCountFrequency (%)
-165.6723 2203
0.1%
-156.292 734
 
< 0.1%
-155.488 1463
0.1%
-155.3697 2186
0.1%
-153.994 12
 
< 0.1%
-133.1171 14
 
< 0.1%
-124.4409 1467
0.1%
-124.2174 2195
0.1%
-124.1587 1465
0.1%
-124.1437 2198
0.1%
ValueCountFrequency (%)
-67.9503 2922
0.2%
-68.5565 1467
 
0.1%
-69.2675 743
 
< 0.1%
-69.4828 2931
0.2%
-69.9576 737
 
< 0.1%
-69.9656 4374
0.2%
-70.1031 9
 
< 0.1%
-70.239 1455
 
0.1%
-70.3001 2924
0.2%
-70.3457 2196
0.1%

city_pop
Real number (ℝ)

Distinct891
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88643.675
Minimum23
Maximum2906700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:29.062389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile139
Q1741
median2443
Q320328
95-th percentile525713
Maximum2906700
Range2906677
Interquartile range (IQR)19587

Descriptive statistics

Standard deviation301487.62
Coefficient of variation (CV)3.4011182
Kurtosis37.572846
Mean88643.675
Median Absolute Deviation (MAD)2188
Skewness5.5908046
Sum1.6420301 × 1011
Variance9.0894784 × 1010
MonotonicityNot monotonic
2025-04-21T00:56:29.286184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
606 8049
 
0.4%
1595797 7312
 
0.4%
1312922 7297
 
0.4%
241 6578
 
0.4%
1766 6556
 
0.4%
2906700 5865
 
0.3%
302 5853
 
0.3%
198 5850
 
0.3%
276002 5849
 
0.3%
1126 5841
 
0.3%
Other values (881) 1787344
96.5%
ValueCountFrequency (%)
23 2915
0.2%
37 1469
 
0.1%
43 2920
0.2%
46 4386
0.2%
47 734
 
< 0.1%
49 1472
 
0.1%
51 1470
 
0.1%
52 740
 
< 0.1%
53 3660
0.2%
60 1472
 
0.1%
ValueCountFrequency (%)
2906700 5865
0.3%
2504700 2929
0.2%
2383912 737
 
< 0.1%
1595797 7312
0.4%
1577385 3680
0.2%
1526206 5113
0.3%
1417793 8
 
< 0.1%
1382480 2913
 
0.2%
1312922 7297
0.4%
1263321 5141
0.3%

job
Text

Distinct497
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:29.684445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length59
Median length38
Mean length20.232398
Min length3

Characters and Unicode

Total characters37478372
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPsychologist, counselling
2nd rowSpecial educational needs teacher
3rd rowNature conservation officer
4th rowPatent attorney
5th rowDance movement psychotherapist
ValueCountFrequency (%)
engineer 188048
 
4.6%
officer 158202
 
3.8%
manager 87837
 
2.1%
scientist 79740
 
1.9%
designer 74639
 
1.8%
surveyor 70288
 
1.7%
teacher 54865
 
1.3%
psychologist 46856
 
1.1%
research 42426
 
1.0%
editor 40958
 
1.0%
Other values (457) 3270295
79.5%
2025-04-21T00:56:30.290477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 4003951
 
10.7%
i 3407729
 
9.1%
r 3140909
 
8.4%
a 2593110
 
6.9%
t 2547852
 
6.8%
n 2521475
 
6.7%
2261760
 
6.0%
o 2133314
 
5.7%
s 2064644
 
5.5%
c 1890653
 
5.0%
Other values (43) 10912975
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37478372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4003951
 
10.7%
i 3407729
 
9.1%
r 3140909
 
8.4%
a 2593110
 
6.9%
t 2547852
 
6.8%
n 2521475
 
6.7%
2261760
 
6.0%
o 2133314
 
5.7%
s 2064644
 
5.5%
c 1890653
 
5.0%
Other values (43) 10912975
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37478372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4003951
 
10.7%
i 3407729
 
9.1%
r 3140909
 
8.4%
a 2593110
 
6.9%
t 2547852
 
6.8%
n 2521475
 
6.7%
2261760
 
6.0%
o 2133314
 
5.7%
s 2064644
 
5.5%
c 1890653
 
5.0%
Other values (43) 10912975
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37478372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4003951
 
10.7%
i 3407729
 
9.1%
r 3140909
 
8.4%
a 2593110
 
6.9%
t 2547852
 
6.8%
n 2521475
 
6.7%
2261760
 
6.0%
o 2133314
 
5.7%
s 2064644
 
5.5%
c 1890653
 
5.0%
Other values (43) 10912975
29.1%

dob
Date

Distinct984
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.1 MiB
Minimum1924-10-30 00:00:00
Maximum2005-01-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-21T00:56:30.481335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:56:30.736780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

trans_num
Text

Unique 

Distinct1852394
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:32.761330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters59276608
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1852394 ?
Unique (%)100.0%

Sample

1st row0b242abb623afc578575680df30655b9
2nd row1f76529f8574734946361c461b024d99
3rd rowa1a22d70485983eac12b5b88dad1cf95
4th row6b849c168bdad6f867558c3793159a81
5th rowa41d7549acf90789359a9aa5346dcb46
ValueCountFrequency (%)
d71c95ab6b7356dd74389d41df429c87 1
 
< 0.1%
1765bb45b3aa3224b4cdcb6e7a96cee3 1
 
< 0.1%
0b242abb623afc578575680df30655b9 1
 
< 0.1%
1f76529f8574734946361c461b024d99 1
 
< 0.1%
a1a22d70485983eac12b5b88dad1cf95 1
 
< 0.1%
6b849c168bdad6f867558c3793159a81 1
 
< 0.1%
a41d7549acf90789359a9aa5346dcb46 1
 
< 0.1%
189a841a0a8ba03058526bcfe566aab5 1
 
< 0.1%
83ec1cc84142af6e2acf10c44949e720 1
 
< 0.1%
6d294ed2cc447d2c71c7171a3d54967c 1
 
< 0.1%
Other values (1852384) 1852384
> 99.9%
2025-04-21T00:56:34.903840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 3708557
 
6.3%
4 3707696
 
6.3%
7 3707599
 
6.3%
2 3707045
 
6.3%
3 3706132
 
6.3%
1 3705118
 
6.3%
d 3704966
 
6.3%
a 3704452
 
6.2%
8 3704258
 
6.2%
c 3703707
 
6.2%
Other values (6) 22217078
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 59276608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 3708557
 
6.3%
4 3707696
 
6.3%
7 3707599
 
6.3%
2 3707045
 
6.3%
3 3706132
 
6.3%
1 3705118
 
6.3%
d 3704966
 
6.3%
a 3704452
 
6.2%
8 3704258
 
6.2%
c 3703707
 
6.2%
Other values (6) 22217078
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 59276608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 3708557
 
6.3%
4 3707696
 
6.3%
7 3707599
 
6.3%
2 3707045
 
6.3%
3 3706132
 
6.3%
1 3705118
 
6.3%
d 3704966
 
6.3%
a 3704452
 
6.2%
8 3704258
 
6.2%
c 3703707
 
6.2%
Other values (6) 22217078
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 59276608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 3708557
 
6.3%
4 3707696
 
6.3%
7 3707599
 
6.3%
2 3707045
 
6.3%
3 3706132
 
6.3%
1 3705118
 
6.3%
d 3704966
 
6.3%
a 3704452
 
6.2%
8 3704258
 
6.2%
c 3703707
 
6.2%
Other values (6) 22217078
37.5%

unix_time
Real number (ℝ)

High correlation 

Distinct1819583
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3586742 × 109
Minimum1.325376 × 109
Maximum1.3885344 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:35.303015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.325376 × 109
5-th percentile1.3300982 × 109
Q11.3430168 × 109
median1.3570893 × 109
Q31.3745815 × 109
95-th percentile1.3867821 × 109
Maximum1.3885344 × 109
Range63158356
Interquartile range (IQR)31564662

Descriptive statistics

Standard deviation18195081
Coefficient of variation (CV)0.013391791
Kurtosis-1.1995793
Mean1.3586742 × 109
Median Absolute Deviation (MAD)15789076
Skewness-0.019735681
Sum2.5168 × 1015
Variance3.3106099 × 1014
MonotonicityNot monotonic
2025-04-21T00:56:35.525808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1335110521 4
 
< 0.1%
1370050667 4
 
< 0.1%
1370177227 4
 
< 0.1%
1381001869 4
 
< 0.1%
1386957227 4
 
< 0.1%
1387312599 4
 
< 0.1%
1387468942 4
 
< 0.1%
1344074858 3
 
< 0.1%
1355636572 3
 
< 0.1%
1336836798 3
 
< 0.1%
Other values (1819573) 1852357
> 99.9%
ValueCountFrequency (%)
1325376018 1
< 0.1%
1325376044 1
< 0.1%
1325376051 1
< 0.1%
1325376076 1
< 0.1%
1325376186 1
< 0.1%
1325376248 1
< 0.1%
1325376282 1
< 0.1%
1325376308 1
< 0.1%
1325376318 1
< 0.1%
1325376361 1
< 0.1%
ValueCountFrequency (%)
1388534374 1
< 0.1%
1388534364 1
< 0.1%
1388534355 1
< 0.1%
1388534349 1
< 0.1%
1388534347 1
< 0.1%
1388534314 1
< 0.1%
1388534284 1
< 0.1%
1388534276 1
< 0.1%
1388534270 1
< 0.1%
1388534238 1
< 0.1%

merch_lat
Real number (ℝ)

High correlation 

Distinct1754157
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.538976
Minimum19.027422
Maximum67.510267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:35.768463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19.027422
5-th percentile29.753795
Q134.740122
median39.3689
Q341.956263
95-th percentile46.002013
Maximum67.510267
Range48.482845
Interquartile range (IQR)7.2161407

Descriptive statistics

Standard deviation5.1056039
Coefficient of variation (CV)0.13247897
Kurtosis0.77423362
Mean38.538976
Median Absolute Deviation (MAD)3.38992
Skewness-0.1880969
Sum71389368
Variance26.067191
MonotonicityNot monotonic
2025-04-21T00:56:35.989236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.545984 4
 
< 0.1%
41.014694 4
 
< 0.1%
40.016559 4
 
< 0.1%
41.973278 4
 
< 0.1%
39.516582 4
 
< 0.1%
41.463521 4
 
< 0.1%
40.062499 4
 
< 0.1%
41.340895 4
 
< 0.1%
38.164527 4
 
< 0.1%
41.522948 4
 
< 0.1%
Other values (1754147) 1852354
> 99.9%
ValueCountFrequency (%)
19.027422 1
< 0.1%
19.027785 1
< 0.1%
19.027804 1
< 0.1%
19.027849 1
< 0.1%
19.029798 1
< 0.1%
19.031242 1
< 0.1%
19.032277 1
< 0.1%
19.032689 1
< 0.1%
19.033288 1
< 0.1%
19.034282 1
< 0.1%
ValueCountFrequency (%)
67.510267 1
< 0.1%
67.441518 1
< 0.1%
67.397018 1
< 0.1%
67.188111 1
< 0.1%
67.064277 1
< 0.1%
66.835174 1
< 0.1%
66.682905 1
< 0.1%
66.679297 1
< 0.1%
66.674714 1
< 0.1%
66.67355 1
< 0.1%

merch_long
Real number (ℝ)

High correlation 

Distinct1809753
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.22794
Minimum-166.67157
Maximum-66.950902
Zeros0
Zeros (%)0.0%
Negative1852394
Negative (%)100.0%
Memory size14.1 MiB
2025-04-21T00:56:36.243075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-166.67157
5-th percentile-119.30928
Q1-96.89944
median-87.440694
Q3-80.245108
95-th percentile-73.365169
Maximum-66.950902
Range99.720673
Interquartile range (IQR)16.654332

Descriptive statistics

Standard deviation13.759692
Coefficient of variation (CV)-0.15249924
Kurtosis1.8312584
Mean-90.22794
Median Absolute Deviation (MAD)8.2235005
Skewness-1.143933
Sum-1.6713769 × 108
Variance189.32913
MonotonicityNot monotonic
2025-04-21T00:56:36.496126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.830842 4
 
< 0.1%
-87.621011 4
 
< 0.1%
-82.223196 4
 
< 0.1%
-90.85685 4
 
< 0.1%
-74.433003 4
 
< 0.1%
-80.893888 4
 
< 0.1%
-95.822621 4
 
< 0.1%
-81.219189 4
 
< 0.1%
-92.521318 4
 
< 0.1%
-74.618269 4
 
< 0.1%
Other values (1809743) 1852354
> 99.9%
ValueCountFrequency (%)
-166.671575 1
< 0.1%
-166.671242 1
< 0.1%
-166.670685 1
< 0.1%
-166.670132 1
< 0.1%
-166.670006 1
< 0.1%
-166.66991 1
< 0.1%
-166.669812 1
< 0.1%
-166.669638 1
< 0.1%
-166.666179 1
< 0.1%
-166.664828 1
< 0.1%
ValueCountFrequency (%)
-66.950902 1
< 0.1%
-66.952026 1
< 0.1%
-66.952352 1
< 0.1%
-66.955602 1
< 0.1%
-66.955996 1
< 0.1%
-66.95654 1
< 0.1%
-66.957364 1
< 0.1%
-66.958659 1
< 0.1%
-66.958751 1
< 0.1%
-66.959178 1
< 0.1%

is_fraud
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.1 MiB
0
1842743 
1
 
9651

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1852394
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1842743
99.5%
1 9651
 
0.5%

Length

2025-04-21T00:56:36.782902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-21T00:56:36.893649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1842743
99.5%
1 9651
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1842743
99.5%
1 9651
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1852394
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1842743
99.5%
1 9651
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1852394
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1842743
99.5%
1 9651
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1852394
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1842743
99.5%
1 9651
 
0.5%

amt_month
Real number (ℝ)

High correlation 

Distinct896534
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4153.689
Minimum1
Maximum43261.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:37.051857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile251.28
Q11344.79
median3071.99
Q35738.47
95-th percentile11792.017
Maximum43261.89
Range43260.89
Interquartile range (IQR)4393.68

Descriptive statistics

Standard deviation3909.0054
Coefficient of variation (CV)0.94109246
Kurtosis6.2031988
Mean4153.689
Median Absolute Deviation (MAD)2005.26
Skewness1.9707692
Sum7.6942686 × 109
Variance15280323
MonotonicityNot monotonic
2025-04-21T00:56:37.274292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.31 15
 
< 0.1%
1.15 15
 
< 0.1%
5.29 15
 
< 0.1%
9.12 14
 
< 0.1%
1.07 14
 
< 0.1%
8.95 14
 
< 0.1%
9.46 14
 
< 0.1%
9.37 13
 
< 0.1%
2.77 13
 
< 0.1%
4.38 13
 
< 0.1%
Other values (896524) 1852254
> 99.9%
ValueCountFrequency (%)
1 4
 
< 0.1%
1.01 8
< 0.1%
1.02 9
< 0.1%
1.03 7
< 0.1%
1.04 6
< 0.1%
1.05 5
 
< 0.1%
1.06 6
< 0.1%
1.07 14
< 0.1%
1.08 9
< 0.1%
1.09 8
< 0.1%
ValueCountFrequency (%)
43261.89 1
< 0.1%
43055.12 1
< 0.1%
43047.94 1
< 0.1%
43013.27 1
< 0.1%
42923.81 1
< 0.1%
42917.54 1
< 0.1%
42887.02 1
< 0.1%
42841.05 1
< 0.1%
42818.8 1
< 0.1%
42750.39 1
< 0.1%

amt_year
Real number (ℝ)

High correlation 

Distinct1694572
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45305.597
Minimum1.02
Maximum219086.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:37.512745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.02
5-th percentile3283.553
Q117341.423
median37439.105
Q364720.88
95-th percentile115831.99
Maximum219086.77
Range219085.75
Interquartile range (IQR)47379.458

Descriptive statistics

Standard deviation35867.522
Coefficient of variation (CV)0.79167972
Kurtosis1.4120611
Mean45305.597
Median Absolute Deviation (MAD)22656.73
Skewness1.1686746
Sum8.3923817 × 1010
Variance1.2864792 × 109
MonotonicityNot monotonic
2025-04-21T00:56:37.734939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5468.43 6
 
< 0.1%
8598.52 5
 
< 0.1%
14612.75 5
 
< 0.1%
1161.57 5
 
< 0.1%
26549.08 5
 
< 0.1%
12498.63 5
 
< 0.1%
9390.26 5
 
< 0.1%
31282.23 5
 
< 0.1%
19724.97 5
 
< 0.1%
13273.31 5
 
< 0.1%
Other values (1694562) 1852343
> 99.9%
ValueCountFrequency (%)
1.02 1
< 0.1%
1.03 1
< 0.1%
1.04 1
< 0.1%
1.07 1
< 0.1%
1.08 1
< 0.1%
1.13 2
< 0.1%
1.15 1
< 0.1%
1.19 1
< 0.1%
1.2 2
< 0.1%
1.21 1
< 0.1%
ValueCountFrequency (%)
219086.77 1
< 0.1%
219073.58 1
< 0.1%
219025.18 1
< 0.1%
218957.58 1
< 0.1%
218955.06 1
< 0.1%
218941.76 1
< 0.1%
218866.61 1
< 0.1%
218824.2 1
< 0.1%
218743.75 1
< 0.1%
218713.06 1
< 0.1%

amt_month_shopping_net_spend
Real number (ℝ)

High correlation  Zeros 

Distinct73861
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean376.2028
Minimum0
Maximum12047.18
Zeros276206
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:37.972300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.02
median75.89
Q3425.98
95-th percentile1717.35
Maximum12047.18
Range12047.18
Interquartile range (IQR)416.96

Descriptive statistics

Standard deviation725.35307
Coefficient of variation (CV)1.9280906
Kurtosis23.999749
Mean376.2028
Median Absolute Deviation (MAD)75.89
Skewness4.0224138
Sum6.9687581 × 108
Variance526137.08
MonotonicityNot monotonic
2025-04-21T00:56:38.225842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 276206
 
14.9%
9.12 575
 
< 0.1%
9.89 528
 
< 0.1%
9.35 475
 
< 0.1%
9.52 469
 
< 0.1%
4.49 465
 
< 0.1%
9.2 451
 
< 0.1%
4.93 448
 
< 0.1%
7.85 436
 
< 0.1%
3.17 434
 
< 0.1%
Other values (73851) 1571907
84.9%
ValueCountFrequency (%)
0 276206
14.9%
1 28
 
< 0.1%
1.01 418
 
< 0.1%
1.02 278
 
< 0.1%
1.03 238
 
< 0.1%
1.04 269
 
< 0.1%
1.05 285
 
< 0.1%
1.06 178
 
< 0.1%
1.07 269
 
< 0.1%
1.08 316
 
< 0.1%
ValueCountFrequency (%)
12047.18 15
< 0.1%
10812.12 3
 
< 0.1%
10805.83 5
 
< 0.1%
10796.17 28
< 0.1%
10790.23 3
 
< 0.1%
10339.78 2
 
< 0.1%
10245.7 11
 
< 0.1%
10242.58 1
 
< 0.1%
10238.88 12
< 0.1%
10235.86 12
< 0.1%

count_month_shopping_net
Real number (ℝ)

High correlation  Zeros 

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5672411
Minimum0
Maximum48
Zeros276206
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:38.447077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile14
Maximum48
Range48
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.5755024
Coefficient of variation (CV)1.0018088
Kurtosis4.3978581
Mean4.5672411
Median Absolute Deviation (MAD)2
Skewness1.7306414
Sum8460330
Variance20.935222
MonotonicityNot monotonic
2025-04-21T00:56:38.763843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 276206
14.9%
1 268131
14.5%
2 227701
12.3%
3 196162
10.6%
4 161418
8.7%
5 135485
7.3%
6 114389
6.2%
7 94026
 
5.1%
8 76806
 
4.1%
9 62216
 
3.4%
Other values (39) 239854
12.9%
ValueCountFrequency (%)
0 276206
14.9%
1 268131
14.5%
2 227701
12.3%
3 196162
10.6%
4 161418
8.7%
5 135485
7.3%
6 114389
6.2%
7 94026
 
5.1%
8 76806
 
4.1%
9 62216
 
3.4%
ValueCountFrequency (%)
48 9
 
< 0.1%
47 8
 
< 0.1%
46 6
 
< 0.1%
45 6
 
< 0.1%
44 7
 
< 0.1%
43 7
 
< 0.1%
42 20
 
< 0.1%
41 33
< 0.1%
40 53
< 0.1%
39 40
< 0.1%

first_time_at_merchant
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
False
1323066 
True
529328 
ValueCountFrequency (%)
False 1323066
71.4%
True 529328
28.6%
2025-04-21T00:56:38.939179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

dist_between_client_and_merch
Real number (ℝ)

Unique 

Distinct1852394
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.109558
Minimum0.022273513
Maximum151.8682
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:39.146825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.022273513
5-th percentile24.764904
Q155.341984
median78.248227
Q398.472041
95-th percentile120.45286
Maximum151.8682
Range151.84593
Interquartile range (IQR)43.130056

Descriptive statistics

Standard deviation29.092731
Coefficient of variation (CV)0.38224806
Kurtosis-0.6320063
Mean76.109558
Median Absolute Deviation (MAD)21.44406
Skewness-0.23773823
Sum1.4098489 × 108
Variance846.387
MonotonicityNot monotonic
2025-04-21T00:56:39.381535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.38098966 1
 
< 0.1%
78.77382075 1
 
< 0.1%
30.21661841 1
 
< 0.1%
108.1029117 1
 
< 0.1%
95.68511548 1
 
< 0.1%
77.70239516 1
 
< 0.1%
86.09735764 1
 
< 0.1%
118.0948553 1
 
< 0.1%
12.75471404 1
 
< 0.1%
25.33388259 1
 
< 0.1%
Other values (1852384) 1852384
> 99.9%
ValueCountFrequency (%)
0.02227351335 1
< 0.1%
0.06673123416 1
< 0.1%
0.09405772594 1
< 0.1%
0.1133855774 1
< 0.1%
0.1241803449 1
< 0.1%
0.1371995071 1
< 0.1%
0.1479746072 1
< 0.1%
0.1538761904 1
< 0.1%
0.2004959165 1
< 0.1%
0.2020716214 1
< 0.1%
ValueCountFrequency (%)
151.8682002 1
< 0.1%
150.6737431 1
< 0.1%
150.5801916 1
< 0.1%
149.6101271 1
< 0.1%
149.2055714 1
< 0.1%
148.6236717 1
< 0.1%
148.6038893 1
< 0.1%
148.5283365 1
< 0.1%
148.4270844 1
< 0.1%
148.1560878 1
< 0.1%

trans_month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.152067
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:39.575415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4249539
Coefficient of variation (CV)0.47887609
Kurtosis-1.1344614
Mean7.152067
Median Absolute Deviation (MAD)3
Skewness-0.13015491
Sum13248446
Variance11.730309
MonotonicityNot monotonic
2025-04-21T00:56:39.762141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 280598
15.1%
8 176118
9.5%
6 173869
9.4%
7 172444
9.3%
5 146875
7.9%
3 143789
7.8%
11 143056
7.7%
9 140185
7.6%
10 138106
7.5%
4 134970
7.3%
Other values (2) 202384
10.9%
ValueCountFrequency (%)
1 104727
5.7%
2 97657
5.3%
3 143789
7.8%
4 134970
7.3%
5 146875
7.9%
6 173869
9.4%
7 172444
9.3%
8 176118
9.5%
9 140185
7.6%
10 138106
7.5%
ValueCountFrequency (%)
12 280598
15.1%
11 143056
7.7%
10 138106
7.5%
9 140185
7.6%
8 176118
9.5%
7 172444
9.3%
6 173869
9.4%
5 146875
7.9%
4 134970
7.3%
3 143789
7.8%

trans_day
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9674562
Minimum0
Maximum6
Zeros369418
Zeros (%)19.9%
Negative0
Negative (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:39.888633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1979833
Coefficient of variation (CV)0.74069612
Kurtosis-1.4581808
Mean2.9674562
Median Absolute Deviation (MAD)2
Skewness0.0077801963
Sum5496898
Variance4.8311304
MonotonicityNot monotonic
2025-04-21T00:56:40.031113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 369418
19.9%
6 343677
18.6%
1 270340
14.6%
5 263227
14.2%
4 215078
11.6%
3 206741
11.2%
2 183913
9.9%
ValueCountFrequency (%)
0 369418
19.9%
1 270340
14.6%
2 183913
9.9%
3 206741
11.2%
4 215078
11.6%
5 263227
14.2%
6 343677
18.6%
ValueCountFrequency (%)
6 343677
18.6%
5 263227
14.2%
4 215078
11.6%
3 206741
11.2%
2 183913
9.9%
1 270340
14.6%
0 369418
19.9%

hour
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.806119
Minimum0
Maximum23
Zeros60655
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:40.189736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median14
Q319
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.8157529
Coefficient of variation (CV)0.53222627
Kurtosis-1.0781663
Mean12.806119
Median Absolute Deviation (MAD)5
Skewness-0.2834188
Sum23721978
Variance46.454488
MonotonicityNot monotonic
2025-04-21T00:56:40.348170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
23 95902
 
5.2%
22 95370
 
5.1%
16 94289
 
5.1%
18 94052
 
5.1%
21 93738
 
5.1%
17 93514
 
5.0%
13 93492
 
5.0%
15 93439
 
5.0%
19 93433
 
5.0%
12 93294
 
5.0%
Other values (14) 911871
49.2%
ValueCountFrequency (%)
0 60655
3.3%
1 61330
3.3%
2 60796
3.3%
3 60968
3.3%
4 59938
3.2%
5 60088
3.2%
6 60406
3.3%
7 60301
3.3%
8 60498
3.3%
9 60231
3.3%
ValueCountFrequency (%)
23 95902
5.2%
22 95370
5.1%
21 93738
5.1%
20 93081
5.0%
19 93433
5.0%
18 94052
5.1%
17 93514
5.0%
16 94289
5.1%
15 93439
5.0%
14 93089
5.0%

year
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.1 MiB
2020
927544 
2019
924850 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters7409576
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2020 927544
50.1%
2019 924850
49.9%

Length

2025-04-21T00:56:40.538209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-21T00:56:40.649036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2020 927544
50.1%
2019 924850
49.9%

Most occurring characters

ValueCountFrequency (%)
2 2779938
37.5%
0 2779938
37.5%
1 924850
 
12.5%
9 924850
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7409576
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2779938
37.5%
0 2779938
37.5%
1 924850
 
12.5%
9 924850
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7409576
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2779938
37.5%
0 2779938
37.5%
1 924850
 
12.5%
9 924850
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7409576
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2779938
37.5%
0 2779938
37.5%
1 924850
 
12.5%
9 924850
 
12.5%

times_shopped_at_merchant
Real number (ℝ)

High correlation 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2980791
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:40.902136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile11
Maximum28
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0943453
Coefficient of variation (CV)0.58405041
Kurtosis1.3990129
Mean5.2980791
Median Absolute Deviation (MAD)2
Skewness1.0238642
Sum9814130
Variance9.5749728
MonotonicityNot monotonic
2025-04-21T00:56:41.092108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4 261036
14.1%
3 259380
14.0%
5 238895
12.9%
2 217440
11.7%
6 200238
10.8%
7 158046
8.5%
1 124202
6.7%
8 122400
6.6%
9 87489
 
4.7%
10 61750
 
3.3%
Other values (15) 121518
6.6%
ValueCountFrequency (%)
1 124202
6.7%
2 217440
11.7%
3 259380
14.0%
4 261036
14.1%
5 238895
12.9%
6 200238
10.8%
7 158046
8.5%
8 122400
6.6%
9 87489
 
4.7%
10 61750
 
3.3%
ValueCountFrequency (%)
28 28
 
< 0.1%
24 72
 
< 0.1%
23 46
 
< 0.1%
22 308
 
< 0.1%
21 462
 
< 0.1%
20 800
 
< 0.1%
19 988
 
0.1%
18 2232
0.1%
17 3094
0.2%
16 4384
0.2%

times_shopped_at_merchant_year
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1504594
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:41.250687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile7
Maximum17
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8653693
Coefficient of variation (CV)0.59209438
Kurtosis1.7758299
Mean3.1504594
Median Absolute Deviation (MAD)1
Skewness1.1527118
Sum5835892
Variance3.4796026
MonotonicityNot monotonic
2025-04-21T00:56:41.615473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 454288
24.5%
3 388647
21.0%
1 355000
19.2%
4 275492
14.9%
5 173405
 
9.4%
6 99906
 
5.4%
7 53319
 
2.9%
8 27648
 
1.5%
9 13221
 
0.7%
10 6010
 
0.3%
Other values (7) 5458
 
0.3%
ValueCountFrequency (%)
1 355000
19.2%
2 454288
24.5%
3 388647
21.0%
4 275492
14.9%
5 173405
 
9.4%
6 99906
 
5.4%
7 53319
 
2.9%
8 27648
 
1.5%
9 13221
 
0.7%
10 6010
 
0.3%
ValueCountFrequency (%)
17 17
 
< 0.1%
16 48
 
< 0.1%
15 150
 
< 0.1%
14 238
 
< 0.1%
13 689
 
< 0.1%
12 1368
 
0.1%
11 2948
 
0.2%
10 6010
 
0.3%
9 13221
0.7%
8 27648
1.5%
Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3891094
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:41.758030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.67225585
Coefficient of variation (CV)0.48394736
Kurtosis4.875747
Mean1.3891094
Median Absolute Deviation (MAD)0
Skewness1.9826927
Sum2573178
Variance0.45192793
MonotonicityNot monotonic
2025-04-21T00:56:41.900483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 1290726
69.7%
2 434458
 
23.5%
3 101286
 
5.5%
4 21056
 
1.1%
5 3980
 
0.2%
6 714
 
< 0.1%
7 140
 
< 0.1%
9 18
 
< 0.1%
8 16
 
< 0.1%
ValueCountFrequency (%)
1 1290726
69.7%
2 434458
 
23.5%
3 101286
 
5.5%
4 21056
 
1.1%
5 3980
 
0.2%
6 714
 
< 0.1%
7 140
 
< 0.1%
8 16
 
< 0.1%
9 18
 
< 0.1%
ValueCountFrequency (%)
9 18
 
< 0.1%
8 16
 
< 0.1%
7 140
 
< 0.1%
6 714
 
< 0.1%
5 3980
 
0.2%
4 21056
 
1.1%
3 101286
 
5.5%
2 434458
 
23.5%
1 1290726
69.7%
Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6554416
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.1 MiB
2025-04-21T00:56:42.059108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.90259006
Coefficient of variation (CV)0.54522617
Kurtosis3.4584246
Mean1.6554416
Median Absolute Deviation (MAD)0
Skewness1.6477251
Sum3066530
Variance0.81466882
MonotonicityNot monotonic
2025-04-21T00:56:42.233558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 1030130
55.6%
2 543102
29.3%
3 196923
 
10.6%
4 59592
 
3.2%
5 16715
 
0.9%
6 4536
 
0.2%
7 1008
 
0.1%
8 280
 
< 0.1%
9 108
 
< 0.1%
ValueCountFrequency (%)
1 1030130
55.6%
2 543102
29.3%
3 196923
 
10.6%
4 59592
 
3.2%
5 16715
 
0.9%
6 4536
 
0.2%
7 1008
 
0.1%
8 280
 
< 0.1%
9 108
 
< 0.1%
ValueCountFrequency (%)
9 108
 
< 0.1%
8 280
 
< 0.1%
7 1008
 
0.1%
6 4536
 
0.2%
5 16715
 
0.9%
4 59592
 
3.2%
3 196923
 
10.6%
2 543102
29.3%
1 1030130
55.6%

Interactions

2025-04-21T00:55:39.769453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:51:13.870205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:51:22.983564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:51:32.339906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:51:44.751175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:51:58.108483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:52:12.482790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:52:25.970196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:52:39.368460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:52:52.692547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:53:06.347900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:53:19.910588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:53:33.020781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:53:46.622647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:54:01.355206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:54:16.770711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:54:29.906579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:54:42.051019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:54:54.342192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:55:08.893168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:55:25.344988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:55:40.512541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:51:14.318906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:51:23.419024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:51:32.771774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:51:45.350611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:51:58.840397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:52:13.099460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:52:26.602694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:52:39.980581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:52:53.293744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:53:07.114132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:53:20.503742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:53:33.624104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:53:47.246391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:54:02.083019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:54:17.497983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:54:30.511477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:54:42.589737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:54:55.175083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:55:09.843770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:55:26.046506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:55:41.151155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:51:14.809074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:51:23.848560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:51:33.216749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:51:45.949994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:51:59.587040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-21T00:54:13.575456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-21T00:52:36.970079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:52:50.222823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:53:03.257265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:53:17.327540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:53:30.464575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:53:43.873501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:53:58.482815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:54:14.213324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:54:27.478128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-21T00:55:37.053930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-21T00:53:31.082400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:53:44.617298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:53:59.178801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:54:14.846230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:54:28.061335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:54:40.336784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:54:52.548730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:55:06.494050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:55:23.129574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:55:37.713655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:55:52.435344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-21T00:52:24.644439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:52:38.157870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:52:51.422776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:53:04.737704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:53:18.636566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-21T00:55:07.121054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-21T00:55:53.106487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:51:22.522873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-21T00:52:25.320185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:52:38.726731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:52:52.044989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:53:05.510272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:53:19.242359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:53:32.381391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:53:45.985838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:54:00.601979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:54:16.139484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:54:29.307896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:54:41.471544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:54:53.739079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:55:08.165845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:55:24.567702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-21T00:55:39.063423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-21T00:56:42.493140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
amtamt_monthamt_month_shopping_net_spendamt_yearcategorycc_numcity_popcount_month_shopping_netdist_between_client_and_merchfirst_time_at_merchantgenderhouris_fraudlatlongmerch_latmerch_longtimes_shopped_at_merchanttimes_shopped_at_merchant_daytimes_shopped_at_merchant_monthtimes_shopped_at_merchant_yeartrans_daytrans_monthunix_timeyearzip
amt1.0000.0610.0810.0370.019-0.001-0.024-0.010-0.0020.0050.001-0.1550.0000.013-0.0000.013-0.0000.0700.0340.0270.0570.001-0.003-0.0010.0020.001
amt_month0.0611.0000.7320.4720.0160.0080.0130.821-0.0000.1670.1060.0670.030-0.012-0.011-0.012-0.0110.2570.1300.1480.212-0.0160.1890.1290.0110.017
amt_month_shopping_net_spend0.0810.7321.0000.3480.0140.010-0.0020.777-0.0000.0650.0910.0480.091-0.005-0.017-0.004-0.0170.1750.0900.0990.144-0.0120.1210.0840.0230.025
amt_year0.0370.4720.3481.0000.0200.0110.0200.4220.0010.3510.1460.0480.036-0.012-0.010-0.012-0.0100.2880.1460.2080.238-0.0040.7920.3960.0110.017
category0.0190.0160.0140.0201.0000.0080.0140.0230.0010.1390.0540.2710.0670.0100.0090.0110.0090.1170.0620.0490.0920.0030.0010.0010.0000.011
cc_num-0.0010.0080.0100.0110.0081.0000.0490.017-0.0000.0190.0520.0110.003-0.003-0.013-0.003-0.0130.0050.0040.0030.005-0.0000.0010.0010.0000.013
city_pop-0.0240.013-0.0020.0200.0140.0491.000-0.0220.0220.0240.0900.0320.002-0.2640.087-0.2630.086-0.016-0.006-0.007-0.0140.000-0.000-0.0030.002-0.040
count_month_shopping_net-0.0100.8210.7770.4220.0230.017-0.0221.000-0.0000.1690.1250.0760.014-0.009-0.026-0.009-0.0260.2600.1330.1450.215-0.0140.1740.1170.0090.029
dist_between_client_and_merch-0.002-0.000-0.0000.0010.001-0.0000.022-0.0001.0000.0000.0050.0010.000-0.070-0.003-0.070-0.0030.0000.0000.0000.000-0.0000.000-0.0000.0010.007
first_time_at_merchant0.0050.1670.0650.3510.1390.0190.0240.1690.0001.0000.0440.0210.0280.0270.0160.0200.0130.3870.2090.2070.3650.0330.2820.5090.3770.025
gender0.0010.1060.0910.1460.0540.0520.0900.1250.0050.0441.0000.0450.0060.1010.0910.1030.0830.1310.0710.0550.1070.0070.0020.0000.0010.116
hour-0.1550.0670.0480.0480.2710.0110.0320.0760.0010.0210.0451.0000.090-0.011-0.005-0.010-0.0050.0230.0090.0050.0170.001-0.0010.0010.0010.006
is_fraud0.0000.0300.0910.0360.0670.0030.0020.0140.0000.0280.0060.0901.0000.0380.0380.0380.0380.0310.0180.0130.0250.0120.0210.0220.0060.004
lat0.013-0.012-0.005-0.0120.010-0.003-0.264-0.009-0.0700.0270.101-0.0110.0381.0000.1050.9910.104-0.013-0.005-0.004-0.0090.001-0.0000.0010.003-0.162
long-0.000-0.011-0.017-0.0100.009-0.0130.087-0.026-0.0030.0160.091-0.0050.0380.1051.0000.1050.998-0.016-0.008-0.007-0.0140.001-0.001-0.0010.003-0.959
merch_lat0.013-0.012-0.004-0.0120.011-0.003-0.263-0.009-0.0700.0200.103-0.0100.0380.9910.1051.0000.104-0.012-0.005-0.004-0.0090.001-0.0000.0010.003-0.162
merch_long-0.000-0.011-0.017-0.0100.009-0.0130.086-0.026-0.0030.0130.083-0.0050.0380.1040.9980.1041.000-0.016-0.008-0.007-0.0130.001-0.001-0.0010.003-0.957
times_shopped_at_merchant0.0700.2570.1750.2880.1170.005-0.0160.2600.0000.3870.1310.0230.031-0.013-0.016-0.012-0.0161.0000.4980.3920.8170.002-0.000-0.0000.0000.019
times_shopped_at_merchant_day0.0340.1300.0900.1460.0620.004-0.0060.1330.0000.2090.0710.0090.018-0.005-0.008-0.005-0.0080.4981.0000.2010.414-0.0160.0010.0010.0000.010
times_shopped_at_merchant_month0.0270.1480.0990.2080.0490.003-0.0070.1450.0000.2070.0550.0050.013-0.004-0.007-0.004-0.0070.3920.2011.0000.323-0.0010.1170.0580.0000.008
times_shopped_at_merchant_year0.0570.2120.1440.2380.0920.005-0.0140.2150.0000.3650.1070.0170.025-0.009-0.014-0.009-0.0130.8170.4140.3231.0000.001-0.0000.0010.0060.016
trans_day0.001-0.016-0.012-0.0040.003-0.0000.000-0.014-0.0000.0330.0070.0010.0120.0010.0010.0010.0010.002-0.016-0.0010.0011.000-0.005-0.0690.125-0.001
trans_month-0.0030.1890.1210.7920.0010.001-0.0000.1740.0000.2820.002-0.0010.021-0.000-0.001-0.000-0.001-0.0000.0010.117-0.000-0.0051.0000.4980.0080.001
unix_time-0.0010.1290.0840.3960.0010.001-0.0030.117-0.0000.5090.0000.0010.0220.001-0.0010.001-0.001-0.0000.0010.0580.001-0.0690.4981.0000.9980.001
year0.0020.0110.0230.0110.0000.0000.0020.0090.0010.3770.0010.0010.0060.0030.0030.0030.0030.0000.0000.0000.0060.1250.0080.9981.0000.002
zip0.0010.0170.0250.0170.0110.013-0.0400.0290.0070.0250.1160.0060.004-0.162-0.959-0.162-0.9570.0190.0100.0080.016-0.0010.0010.0010.0021.000

Missing values

2025-04-21T00:55:55.083739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-21T00:56:02.322135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

cc_nummerchantcategoryamtfirstlastgenderstreetcitystateziplatlongcity_popjobdobtrans_numunix_timemerch_latmerch_longis_fraudamt_monthamt_yearamt_month_shopping_net_spendcount_month_shopping_netfirst_time_at_merchantdist_between_client_and_merchtrans_monthtrans_dayhouryeartimes_shopped_at_merchanttimes_shopped_at_merchant_yeartimes_shopped_at_merchant_monthtimes_shopped_at_merchant_day
02703186189652095fraud_Rippin, Kub and Mannmisc_net4.97JenniferBanksF561 Perry CoveMoravian FallsNC2865436.0788-81.17813495Psychologist, counselling1988-03-090b242abb623afc578575680df30655b9132537601836.011293-82.04831504.974.970.00.0True78.77382111020195421
1630423337322fraud_Heller, Gutmann and Ziemegrocery_pos107.23StephanieGillF43039 Riley Greens Suite 393OrientWA9916048.8878-118.2105149Special educational needs teacher1978-06-211f76529f8574734946361c461b024d99132537604449.159047-118.1864620107.23107.230.00.0True30.21661811020194411
238859492057661fraud_Lind-Buckridgeentertainment220.11EdwardSanchezM594 White Dale Suite 530Malad CityID8325242.1808-112.26204154Nature conservation officer1962-01-19a1a22d70485983eac12b5b88dad1cf95132537605143.150704-112.1544810220.11220.110.00.0True108.10291211020194311
33534093764340240fraud_Kutch, Hermiston and Farrellgas_transport45.00JeremyWhiteM9443 Cynthia Court Apt. 038BoulderMT5963246.2306-112.11381939Patent attorney1967-01-126b849c168bdad6f867558c3793159a81132537607647.034331-112.561071045.0045.000.00.0True95.68511511020191111
4375534208663984fraud_Keeling-Cristmisc_pos41.96TylerGarciaM408 Bradley RestDoe HillVA2443338.4207-79.462999Dance movement psychotherapist1986-03-28a41d7549acf90789359a9aa5346dcb46132537618638.674999-78.632459041.9641.960.00.0True77.70239511020196111
54767265376804500fraud_Stroman, Hudson and Erdmangas_transport94.63JenniferConnerF4655 David IslandDublinPA1891740.3750-75.20452158Transport planner1961-06-19189a841a0a8ba03058526bcfe566aab5132537624840.653382-76.152667094.6394.630.00.0True86.09735811020192211
630074693890476fraud_Rowe-Vandervortgrocery_net44.54KelseyRichardsF889 Sarah Station Suite 624HolcombKS6785137.9931-100.98932691Arboriculturist1993-08-1683ec1cc84142af6e2acf10c44949e720132537628237.162705-100.153370044.5444.540.00.0True118.09485511020194411
76011360759745864fraud_Corwin-Collinsgas_transport71.65StevenWilliamsM231 Flores Pass Suite 720EdinburgVA2282438.8432-78.60036018Designer, multimedia1947-08-216d294ed2cc447d2c71c7171a3d54967c132537630838.948089-78.540296071.6571.650.00.0True12.75471411020193211
84922710831011201fraud_Herzog Ltdmisc_pos4.27HeatherChaseF6888 Hicks Stream Suite 954ManorPA1566540.3359-79.66071472Public affairs consultant1941-03-07fc28024ce480f8ef21a32d64c93a29f5132537631840.351813-79.95814604.274.270.00.0True25.33388311020192111
92720830304681674fraud_Schoen, Kuphal and Nitzschegrocery_pos198.39MelissaAguilarF21326 Taylor Squares Suite 708ClarksvilleTN3704036.5220-87.3490151785Pathologist1974-03-283b9014ea8fb80bd65de0b1463b00b00e132537636137.179198-87.4853810198.39198.390.00.0True73.93971411020195412
cc_nummerchantcategoryamtfirstlastgenderstreetcitystateziplatlongcity_popjobdobtrans_numunix_timemerch_latmerch_longis_fraudamt_monthamt_yearamt_month_shopping_net_spendcount_month_shopping_netfirst_time_at_merchantdist_between_client_and_merchtrans_monthtrans_dayhouryeartimes_shopped_at_merchanttimes_shopped_at_merchant_yeartimes_shopped_at_merchant_monthtimes_shopped_at_merchant_day
185238430344654314976fraud_Larkin, Stracke and Greenfelderentertainment46.71ChristineJohnsonF8011 Chapman Tunnel Apt. 568Blairsden-GraeagleCA9610339.8127-120.64051725Chartered legal executive (England and Wales)1967-05-27a7105564935ea3977dc61ff9ced3bf5e138853423838.963543-120.45712107420.5741336.211706.739.0False95.5903411232320203221
18523853524574586339330fraud_Heathcote, Yost and Kertzmannshopping_net29.56AshleyCabreraF94225 Smith Springs Apt. 617Vero BeachFL3296027.6330-80.4031105638Librarian, public1986-05-079fc9f6f9be3182d519a61a119cf97199138853427027.593881-80.855092014501.2899329.662299.3220.0False44.8264861232320203221
1852386341546199006537fraud_Schmidt-Larkinhome12.68MarkBrownM8580 Moore CoveWalesAK9978364.7556-165.6723145Administrator, education1939-11-09a8310343c189e4a5b6316050d2d6b014138853427665.623593-165.18603308706.2367211.9868.225.0False99.4207571232320204411
1852387501802953619fraud_Pouros, Walker and Spencerkids_pets13.02RobertFloresM3277 Fields Meadows Apt. 790GreenviewCA9603741.5403-122.9366308Call centre manager1958-09-20bd7071fd5c9510a5594ee196368ac80e138853428441.973127-123.55303209016.4365502.891161.1117.0False70.2794501232320204211
18523883523843138706408fraud_Prosacco, Kreiger and Kovacekhome17.00GraceWilliamsF28812 Charles Mill Apt. 628PlantersvilleAL3675832.6176-86.94751412Drilling engineer1970-11-206d04313bfe4b661b8ca2b6a499a320fe138853431432.164145-87.539669013874.1978212.661393.0615.0False75.0531551232320207322
185238930560609640617fraud_Reilly and Sonshealth_fitness43.77MichaelOlsonM558 Michael EstatesLurayMO6345340.4931-91.8912519Town planner1966-02-139b1f753c79894c9f4b71f04581835ada138853434739.946837-91.333331011619.6372134.231014.4411.0False77.0324671232320206311
18523903556613125071656fraud_Hoppe-Parisiankids_pets111.84JoseVasquezM572 Davis MountainsLake JacksonTX7756629.0393-95.440128739Futures trader1999-12-272090647dac2c89a1d86c514c427f5b91138853434929.661049-96.186633015224.4787115.433942.7825.0False100.0237361232320205311
18523916011724471098086fraud_Rau-Robelkids_pets86.88AnnLawsonF144 Evans Islands Apt. 683BurbankWA9932346.1966-118.90173684Musician1981-11-296c5b7c8add471975aa0fec023b2e8408138853435546.658340-119.715054026233.12165389.302978.9129.0False80.88781212323202010712
18523924079773899158fraud_Breitenberg LLCtravel7.99EricPrestonM7020 Doyle Stream Apt. 951MesaID8364344.6255-116.4493129Cartographer1965-12-1514392d723bb7737606b2700ac791b7aa138853436444.470525-117.080888011787.7190698.65768.6917.0False53.0608821232320204221
18523934170689372027579fraud_Dare-Marvinentertainment38.13SamuelFreyM830 Myers Plaza Apt. 384EdmondOK7303435.6665-97.4798116001Media buyer1993-05-101765bb45b3aa3224b4cdcb6e7a96cee3138853437436.210097-97.036372013871.45116400.29883.3118.0False72.3809901232320202111